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Effect of Large-Loss Thresholds on Aggregate Insurance Loss Estimates

Abstract

Large-loss thresholds refer to predetermined claim amounts used to identify and separately analyse unusually large insurance losses within a portfolio. The treatment of large claims is important in actuarial analysis because individual high-severity losses can have a substantial influence on the distribution and total amount of aggregate insurance losses. The selection of an appropriate threshold may therefore affect the estimation of aggregate losses and the assessment of insurance risk. This study will examine the effect of large-loss thresholds on aggregate insurance loss estimates. It will assess how different threshold levels influence the identification, treatment, and estimation of large claims within an insurance portfolio. The study will also examine variations in aggregate loss estimates resulting from the application of alternative large-loss thresholds to historical claims data. The study will focus on large-loss thresholds, aggregate insurance losses, claim severity, claim frequency, large claims, loss distributions, aggregate loss modelling, insurance risk, claims data, and actuarial loss estimation. Historical claims will be classified using different large-loss thresholds, and the resulting claim distributions will be analysed. Actuarial and statistical techniques will then be applied to estimate aggregate insurance losses under alternative threshold levels. A quantitative research approach will be adopted for the study. Historical insurance claims data will be analysed using descriptive statistics, claim frequency and severity analysis, probability distributions, aggregate loss models, threshold analysis, and sensitivity analysis. Aggregate loss estimates generated under different large-loss thresholds will be compared to determine the extent to which threshold selection affects projected insurance losses. The study is expected to reveal that large-loss thresholds may have a significant effect on aggregate insurance loss estimates. Lower thresholds may classify a greater number of claims as large losses, while higher thresholds may focus the analysis on only the most severe claims. The resulting differences in aggregate loss estimates may depend on the frequency and severity of large claims, the underlying loss distribution, portfolio characteristics, and the quality of historical claims data. The study will be useful to actuaries, insurance companies, underwriters, claims managers, risk managers, regulators, and researchers. It may provide useful information for selecting appropriate large-loss thresholds, improving aggregate loss modelling, assessing claim severity, and strengthening insurance risk management. The findings may also assist insurers in evaluating the sensitivity of aggregate loss estimates to alternative approaches for identifying large claims. The study concludes that large-loss threshold selection is an important consideration in aggregate insurance loss estimation because the threshold used to identify and treat severe claims can influence the resulting loss distribution and aggregate loss estimates. It is therefore recommended that insurers and actuaries carefully evaluate historical claims characteristics, loss severity patterns, data quality, and portfolio structure when selecting large-loss thresholds for aggregate insurance loss modelling.

Keywords: Large-loss thresholds, aggregate insurance loss estimates, claim severity, claim frequency, large claims, loss distributions, aggregate loss modelling, insurance risk, claims data, actuarial loss estimation, severe claims, loss threshold, aggregate claims, insurance portfolio, actuarial analysis.